Integrated Computer-Aided Engineering - Volume 17, issue 3

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ISSN 1069-2509 (P)
ISSN 1875-8835 (E)

Impact Factor 2018: 3.667

The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality.

Abstract: The percentage of shop fabrication, including pipe spool fabrication, has been increasing on industrial construction projects in the past years. Industrial fabrication has a great impact on construction projects due to the fact that the productivity is higher in a controlled environment than in the field, and therefore time and cost of construction projects are reduced by making use of industrial fabrication. Effective planning and scheduling of industrial fabrication processes is important for the success of construction projects. Dispatching rules are among the common methods for optimizing and improving the performance of complicated systems such as industrial fabrication shops. However,…the performance of dispatching rules varies under different operating conditions and scenarios, such as changes in processing times of jobs (e.g., spools, modules), and changes in the system's configurations (e.g., number of stations and resources on the shop floor in pipe spool fabrication). This paper focuses on developing a new framework for optimizing shop scheduling, particularly pipe spool fabrication shop scheduling, which uses the Pareto-optimality concept combined with fuzzy set theory for multi-criteria (i.e., multi-objective) optimization. The proposed framework makes it possible to capture uncertainty of a pipe spool fabrication shop while accounting for linguistic vagueness of the decision makers' preferences, using simulation modeling and fuzzy set theory. The implementation of the proposed framework is discussed using a real case study of a pipe spool fabrication shop.
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Abstract: In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer…estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data.
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Abstract: Support calculation and duplicate detection are the most challenging and unavoidable subtasks in frequent connected subgraph (FCS) mining. The most successful FCS mining algorithms have focused on optimizing these subtasks since the existing solutions for both subtasks have high computational complexity. In this paper, we propose two novel properties that allow removing all duplicate candidates before support calculation. Besides, we introduce a fast support calculation strategy based on embedding structures. Both properties and the new embedding structure are used for designing two new algorithms: gdFil for mining all FCSs; and gdClosed for mining all closed FCSs. The experimental results show…that our proposed algorithms get the best performance in comparison with other well known algorithms.
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Abstract: This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and…sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.
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Abstract: Recent advances in Artificial Intelligence imply challenges and opportunities to explore new kinds of artistic experiences in interaction with the spectator of an artistic installation. In this context, a wide range of elements, such as sensors and speech recognition and synthesis, has been considered to establish an intelligent environment with an artistic purpose. The coordination of these elements to create an interactive environment where the spectator may feel faced with some kind of human-like behavior (the use of speech contributes to this goal) requires the integration of different Artificial Intelligence techniques. The definition of Talking Agents as reusable components for…managing coordination of this diversity of elements and the interaction with the spectator facilitates building and setting different artistic scenarios. This paper describes the architecture of Talking Agents and their implementation in a multi-agent framework. This has been used in an experiment with an artistic installation called ORACULOS, and the results have been the basis for considering the evolution and application of the Talking Agent architecture in Ambient Assisted Living scenarios.
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Abstract: Color reduction in images is an important problem in image processing, since it is a pre-processing step in applications such as image segmentation or compression. Different methods have been proposed in the literature, several of them involving nature-inspired algorithms such as neural networks. However, not many works involving evolutionary computation techniques have been applied to this problem. This paper proposes a novel evolutionary algorithm to tackle the color reduction of RGB images. The proposed evolutionary algorithm incorporates a procedure called incremental-encoding, consisting in starting the image quantization with a small number of colors, and including additional colors in a gradual…form, until reaching the final number of quantization colors. In the experiments carried out we show that the incremental-encoding evolutionary algorithm improves the performance of the standard evolutionary algorithm in this problem. Also we show that it obtains better results than several existing color reduction techniques for color quantization problems.
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